Data-Driven Imputation Method for Traffic Data in Sectional Units of Road Links

Missing data imputation is a critical step in data processing for intelligent transportation systems. This paper proposes a data-driven imputation method for sections of road based on their spatial and temporal correlation using a modified k- nearest neighbor method. This computing-distributable imputation method is different from the conventional algorithms in the fact that it attempts to impute missing data of a section with multiple sensors that have correlation to each other, at once. This increases computational efficiency greatly compared with other methods, whose imputation subject is individual sensors. In addition, the geometrical property of each section is conserved; in other words, the continuation of traffic properties that each sensor captures is conserved, therefore increasing accuracy of imputation. This paper shows results and analysis of comparison of the proposed method to others such as nearest historical data and expectation maximization by varying missing data type, missing ratio, traffic state, and day type. The results show that the proposed algorithm achieves better performance in almost all of the missing types, missing ratios, day types, and traffic states. When the missing data type cannot be identified or various missing types are mixed, the proposed algorithm shows accurate and stable imputation performance.